
Causal mediation analysis for stochastic interventions
Mediation analysis in causal inference has traditionally focused on bina...
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A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data
Segmented regression is a standard statistical procedure used to estimat...
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Improving the Accuracy of Confidence Intervals and Regions in Multivariate Randomeffects Metaanalysis
Multivariate randomeffects metaanalyses have been widely applied in ev...
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Robust Inference for Mediated Effects in Partially Linear Models
We consider mediated effects of an exposure, X on an outcome, Y, via a s...
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Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator
The HighlyAdaptiveLASSO Targeted Minimum Loss Estimator (HALTMLE) is ...
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General Finite Sample Inference for Experiments with Examples from Health Care
I exploit knowledge of the randomization process within an experiment to...
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Posterior Average Effects
Economists are often interested in computing averages with respect to a ...
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Nonparametric inference for interventional effects with multiple mediators
Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estimators of these effects are based on parametric models with confidence interval estimation facilitated via the nonparametric bootstrap. We provide theory that allows for more flexible, possibly machine learningbased, estimation techniques to be considered. In particular, we establish weak convergence results that facilitate the construction of closedform confidence intervals and hypothesis tests. Finally, we demonstrate multiple robustness properties of the proposed estimators. Simulations show that inference based on largesample theory has adequate smallsample performance. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.
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